Research and Innovation

Data Science

Innovation in Data Engineering and Science (IDEAS)

IDEAS will commit $60 million in resources for faculty hiring and research in the areas of data-driven scientific discovery and experimentation, design and engineering of safe, explainable and trustable autonomous systems, and data science for neuro engineering and bio-inspired computing.

IDEAS Faculty Search in Artificial Intelligence, Data Science and Machine Learning

The School of Engineering and Applied Science at the University of Pennsylvania is growing its faculty thanks to a major $750M investment in science, engineering and medicine. As part of this initiative, the Center for Innovation in Data Engineering and Science (IDEAS) is engaged in an aggressive hiring effort for multiple tenured or tenure-track faculty positions in Artificial Intelligence, Data Science, and Machine Learning. Applications open mid-fall and will be due December 1. Areas of interest include but are not limited to:

  1. Foundations of AI/ML/DS: understanding the mathematical foundations of AI/ML/DS to enable the development of the next generation of data-driven methods.
  2. Scientific AI/ML/DS: data-driven approaches that can transform scientific discovery and modeling of new phenomena across engineering and science.
  3. Bio-inspired AI/ML/DS: developing new paradigms for bridging the gap between human and machine learning, bio-inspired computing, and AI in health.
  4. Trustworthy AI/ML/DS: design and engineering of fair, ethical, explainable, robust, safe, and trustworthy autonomous systems.

Post-Doctoral Positions

IDEAS is also engaged in hiring efforts for Post-Doctoral Positions in AI, Data Engineering, and Science.

Leadership

René Vidal, Rachleff and Penn Integrates Knowledge University Professor in the Department of Electrical and Systems Engineering in the School of Engineering and Applied Science, and the Department of Radiology in the Perelman School of Medicine, is IDEAS inaugural director. Vidal‘s research focuses on the mathematical foundations of deep learning and its applications in computer vision and biomedical data science. In addition to bridging data engineering and science activities across all of Penn’s schools, IDEAS is actively recruiting core faculty with expertise in those respective areas.

Facilities

Amy Gutmann Hall is the new home for data science at Penn, serving as a hub for infusing data-driven approaches  into every department and sparking new collaborations,  both with industry partners and the next generation of data scientists in Philadelphia’s public schools.

Affiliated Faculty: